October 2, 2018 • 36 mins
Listener Dan wanted to know more about Apple's "neural engine." What exactly is it, and what can it do? We explore how a system on a chip can power machine learning in a smartphone.
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Speaker 1 (00:04):
Get in touch with technology with tech Stuff from how
stuff works dot com. Hey there, and welcome to tech Stuff.
My name is Jonathan Strickland. I happen to be the
host of this show. I'm also an executive producer at
how stuff Works. And hey, I love all things tech,
and today we're doing a little listener mail request. Dan
(00:27):
wrote in and asked if I might do an episode
about Apple's so called neural engine in its more recent iPhones.
So today we are going to learn what a neural
engine is and what it does. And if you guys,
by the way, have any requests for topics you've always thought, Hey,
I want to have an episode about this particular tech topic. Remember,
(00:49):
you can send those to me by sending an email
to tex Stuff at how stuff works dot com. And
now let's talk about this neural engine. Well, the general
public for heard about this topic back in September two
thousand seventeen, when Apple CEO Tim Cook presented at what
has become an annual tradition for Apple at around that
(01:12):
time of year, pretty much every September is when Apple
will come out and unveil the latest in its line
of iPhone smartphones, and in that would have been the
Iconic iPhone X, the tenth anniversary edition of the iPhone
also the one that's been discontinued now. Cook listed off
(01:32):
a lot of features when he went to that presentation,
but the one we're really interested in today is part
of the phones A eleven micro processor, also called the
A eleven Bionic CPU. The most recent iPhones as of
the recording of this podcast now have the next generation
of that chip, the A twelve, But in both cases,
(01:55):
the neural engine is one of the elements that gets
a lot of coverage. So let's go to the A
eleven since that was the first one to have it.
It's more than just a CPU. It's technically a system
on a chip or s O a C. It's an
ARM sixty four bit chip. But that doesn't really tell
you anything if you're not, you know, deep into the
(02:18):
world of micro processors. So what does that actually mean. Well,
the ARM based part means that it's it's based on
the ARM micro architecture in chip design. So for our
purposes we can simplify this to say, the chips components,
the stuff that's actually on the microprocessor are laid out
(02:38):
in a way that was developed by ARM Holdings, that's
the company behind ARM processors. Now that is different from
the layout you would find in a chip that was
made by Intel, for example. So the architecture part literally
refers to the layout of components in the microprocessor and
how they interact with each other. And generally speaking, companies
(03:02):
that make microprocessors develop an architecture. They do so in
a way that is supposed to maximize the efficiency of
the chips. So if you get the most power for
the least amount of energy input you can with the
least amount of waste, really is the best way of
putting it. You don't want to waste too much and
produce too much heat. And then you typically would then
(03:25):
reduce the size of the various components. And then after
you reduce the size of the components, you might figure
out a new architecture that makes better use of these
smaller components. And this process goes on and on. Intel
calls this the TIC talk methodology. So that's what the
ARM based part means. It's from this particular company following
(03:47):
this particular layout. As for that sixty four bit part,
what does that mean, Well, that refers to the data
width of the arithmetic logic unit or a LU. The
says the part of the processor that actually carries out
those operations on data from computer instructions. So data with
(04:08):
essentially tells you how much information the a L you
can accept or handle at a given time, and it
tells you this in bits. Now, a bit, just to
remind you, is a single unit of computational information, and
it is binary, meaning has two states, which we designate
(04:29):
as being either a zero or a one. Some people
say often on or false and true, but it's zero
and one. The number of bits tells you how big
these actual numbers can get. Before the a L you
can't handle them anymore. So let's say you have an
(04:49):
eight bit chip, because that's a lot easier to talk about.
You would be able to add, subtract, multiply, divide, you know,
the basic arithmetic lot logical operation to eight bit numbers.
With an eight bit chip, Now, a single bit is
a zero or a one, and eight bit number you
can represent as a string of eight eight numbers, either
(05:14):
zeros or ones, So you could have eight zeros in
a row, up to eight ones in a row and
everything in between. So it could be seven zeros and
then a one or it could be six zeros and
then a one and then another zero. You get the point.
With that many combinations, that means you would be able
to go from the typical numbers of zero to two
(05:36):
hundred fifty five. That's with eight bit. However, we're not
talking eight bit. We're talking about a sixty four bit chips.
So now you have sixty four digits in a row
that can be either a zero or one. That provides
you a lot more combinations, which means you could range
(05:57):
in number from zero row to nine quintillion, two hundred
twenty three quadrillion, three hundred seventy two trillion, thirty six billion,
eight hundred fifty four million, seven hundred seventy five thousand,
eight hundred seven. That's a pretty big range. It can
(06:19):
handle way way larger numbers than an eight bit chip.
So that tells you the type of architecture this chip
has and the amount of data it can handle at
a time. The A eleven has six cores, so processors
with multiple cores can work on parts of a problem simultaneously.
(06:40):
If you have something that's called a parallel problem, you
can divide that problem up into different segments and have
different cores tackle it. Two of those six cores are
what Apple calls high performance cores. They have a clock
speed of two point three thirty nine giga hurts uh
in the A eleven, So the clock speed tells you
how many clock cycles a CPU can perform per second.
(07:03):
Two point three nine gigga hurts means that these cores
can each perform two point thirty nine billion clock cycles
per second. Now, clock cycles do not easily translate over
into actions. It's not necessarily one clock cycle per action.
But generally these numbers tell you how much a core
(07:26):
of the processor is able to handle per second, how
many tasks it can do per second, assuming a certain
number of clock cycles per task. Now, these two cores
are referred to as Monsoon. The other four cores are
what Apple refers to as energy efficient cores. They are
not at that same high clock speed. They are meant
(07:47):
to handle more routine tasks. They are called mistral. So
you have Monsoon and Mistral, two Monsoon cores for mistral cores.
But the A eleven is not just a CPU. Also
has a three core graphics processing unit or GPU incorporated
into this chip. And then there are the two processing
(08:08):
cores dedicated specifically to handling tasks related to machine learning algorithms.
This pair of processors are the neural engine. They are
essentially an artificial neural network. And I've talked a little
bit about artificial neural networks before, but we're really going
to try and get an understanding of what makes them
(08:29):
special today, because that's really why neural engine means anything
in the first place. So this means we get to
do a quick history lesson because this is tech stuff,
and of course we have to go into the history.
So here we go back in the nineteen forties and
the nineteen fifties, there were some smart guys named Warren
(08:51):
McCullough who was a neurophysiologist, and another guy named Walter
Pitts who was a computer scientist and a logician, and
they began developing theories that brought together computational science and neuroscience,
in other words, the way machines process information and the
way brains process information, which is different. McCullough wrote a
(09:13):
couple of papers about this, and he asserted that the
basic unit of logic in the brain is the neuron.
So the nerve cell, the brain cell, is your your
basic unit of logic in a brain, so it would
act kind of like a gate or a transistor in
a circuit. And so you might have a transistor being
(09:35):
the smallest unit, not not metric of logic, but the
smallest unit to allow this to happen in a circuit
neurons in the brain. Pets and McCullough began developing computer
algorithms that attempted to guide machines to process information in
a way that was at least conceptually similar to the
way our brains process information. McCullough had proposed that by
(09:57):
doing this, you could train a machine to wreck niye
handwritten characters like numbers or letters, even if those representations
varied in size or style. And I've talked about this
being a challenge in the past as well, that training
a computer to recognize a specific type of image or
(10:18):
a specific thing in an image is challenging. So I
always use coffee mugs as an example. I don't know why,
but I like that that particular one. So we're gonna
go with it again. If you were to create a
computer program where you feed an image of a coffee
mug to the computer program, and you tell the computer
program this image corresponds with this concept called coffee mug.
(10:43):
And the image shows a blue coffee mug and its
handle is pointed toward the right of the perspective of
the viewer. And then you were to feed a different image,
maybe of that same coffee mug, but now at a
different angle. Well, the machine as looking at this as
if it's a totally new thing. It cannot just uh
(11:05):
extricate that information and say, oh, this is also a
coffee mug, or maybe it's a different coffee mug. It's
a different color or a different size or different shape.
The computer doesn't understand the concept of coffee mug. So
how can you teach it this concept? How can you
train it so it recognizes coffee mugs? That was what
(11:25):
McCulloch was looking at. Then you have another guy who
came along, Frank Rosenblat, very smart man, who built on
this work. He developed an artificial neuron called the perceptron. Now,
a perceptron's job is, from a very high level, pretty simple.
It accepts multiple binary inputs. So it accepts inputs that
(11:46):
are either zeros or ones, and then it produces a
single binary output either a zero or a one based
upon processing that information. So let's say you want to
create a program that can help you decide which restaurant
you want to go to, and you've come up with
three criteria that you think are really important in order
(12:07):
for you to make this decision. And the three criteria
you have are is the restaurant within a twenty minute
drive or less? So, is it relatively close? Will a
meal cost less than fifty dollars for two people to
have dinner there? And does the restaurant serve tacos? Those
are your three points of criteria, and you can represent
(12:30):
each of those variables with a binary figure. So, for example,
you could say that if the restaurant is closer than
a twenty minute drive, if it is nearby, you represent
that variable with a one. If it is further away
than that, it's a zero. If the dinner for two
is cheaper than fifty dollars, that's a one. If it's
(12:50):
more expensive, it's a zero. And if it serves tacos,
it's a one. And if it does not serve tacos,
it's a big fat zero. Then you have a list
of various restaurants you could feed each restaurant through your
criteria and see how they do. Uh, And then you
could narrow your choices this way, and perhaps there is
no single restaurant that meets all those criteria, so you
(13:13):
really should take another step. And that's where Rosenblatt introduces
the concept of weights, where you you change how important
each of the criteria are in relation to each other.
Weights are real numbers that indicate the importance of particular criterion.
So you want, let's say all those three criteria you've identified,
(13:36):
the distance, the cost, and whether or not they have tacos.
You have decided the most critical piece of information is
whether or not the restaurant serves tacos. So you could
then assign a greater weight to that criterion, saying this
is more important to me, and that will influence the
output of the neuron. You must also determine a threshold
(13:58):
value for the decision. In other words, you say, in
order to produce a positive result to tell me, yes,
this is a restaurant you should go to, you must
at least meet this threshold. That's the minimum value the
calculation has to meet or exceed in order to produce
a go to this restaurant result. I'll explain a bit
(14:19):
more about this in just a second, But first I'm
going to take a quick break and thank our sponsors.
That threshold value that I mentioned before the break is
really important because it tells your model what sort of
results count as valid versus not valid. So let's say
(14:43):
I've waited the criteria so that the distance to the
restaurant and the expense of the meal each have a
weight of two, but the presence of tacos is a six.
That's how important I think tacos are. And I've said
a threshold of four. Well, that means that if the
restaurant is relatively close and it's relatively inexpensive, it's going
(15:04):
to pass my criteria because I've given a weight of
two for both of those and added together that's four.
It equals the threshold. Good to go. But even if
the restaurant is far away and even if it's expensive,
if it serves tacos, it still passes my criteria because
I gave the tacos a weight of six. Raising the
threshold value reduces the number of valid restaurants. So if
(15:29):
I make the threshold eight instead of four, now the
only way I can get a valid result a result
of yes, go to this restaurant is if the restaurant
has tacos and it's either close by, or it's inexpensive,
or both. And if I said the threshold were ten,
all three criteria would need to be met for this
(15:51):
option to be valid. Now, an artificial intelligence for the
purposes of notation, many people will move the threshold value
to the other side of the equation, and in this
case we now call it a bias, and a bias
essentially is a measurement to tell you how easy or
difficult it is to get the perceptron to fire off
a positive value. If you have a big positive bias,
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that means it's easier for the perceptron to produce a
positive result a one. A large negative bias does the opposite,
and thus you would get a zero. So we can
write out the perceptron's rules like this. Take the value
of a variable which is either going to be a
zero or a one. It will be binary. You multiply
(16:36):
the value of this variable by the weight of that variable,
and weights can be different values. Let's say that the
distance and expense are both weighted at two. Tacos gets
a big hefty six. You're going to add your various
(16:56):
weighted variable results together, and then you add the I
s for the perceptron. And in our example, the bias
is a minus six. That's to tell us that in
order for this perceptron to fire, you have to you
have to be able to factor in that minus six
and beat it. So if after adding these elements together,
(17:18):
you get a result that is zero or lower, the
output is a zero or a negative, saying, don't go
to this restaurant. So after adding that negative six, if
you have a zero or less, you don't go. If
you get a result that's greater than zero, it's a
positive result, it says, go to that restaurant. So here
in our hypothetical perceptron, we've decided on a bias of
minus six, and we take our three variables as we
(17:40):
examine a single restaurant. So this restaurant is twenty five
minutes away. So that means for our first variable, which
is all about distance, it gets a zero because it
is further than twenty minutes away. So that variable is
a zero. And we multiply the variable times the weight.
The weight is too for that particular variable two time
zero is zero. Then I look and I see that
(18:05):
dinner for two of that restaurant's gonna set me back
thirty dollars, but that's below the limit we had set
of fifty dollars. So that means the value of the
variable is one. It is cheaper than fifty dollars, so
that gets a one. The weight for this variable is
to so multiply the weight times of variable two times
one is two. Then we have the question does the
(18:26):
restaurants serve tacos? And I know you're dying to know this.
I'm glad to report the restaurant does in fact serve tacos,
And that means that the variable is a one. It's positive,
and we waited this variable very heavily with a six,
So six times one is six. Now we have to
add all of those results together, so we have zero
(18:49):
from the first one, too, from the second one, six
from the third one. Add that together you get eight.
Now we have to add in the bias, and the
bias for this perceptron is a minus six. Eight plus
minus six gives us a final value of two. Two
is greater than zero. So by the rules we have established,
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the perceptron says this is a positive result and fires
off a one. So the restaurant we fed to the
perceptron met the criteria based on that bias. Now, if
our bias had been minus ten or minus nine, we
would have not produced this positive result. We have gotten
a zero or negative number and it would have said no.
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So that bias is very important, as is the weight
of the various variables. And that is one neuron. Now
you can actually create layers of neurons. That's why we
call it an artificial neural network, not just an artificial neuron.
And by doing that you can have results from one
neuron's decisions feed directly into another neuron. Also, a perceptron
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can perform as a type of logical gait called a
name end gate in a n D that stands for
not and it's a type of logical gate that can
produce a false or negative output if all its inputs
are true or positive. So, in other words, with the
(20:16):
right weights and biases, a perceptron will produce an output
of zero if all of its inputs are ones. The
nand gate in computer science is a universal gate because
you can use different creations and combinations of nand gates
and build any kind of computation. You just have to
(20:36):
link them together properly in order to do it. It's
not always the most efficient way to do this, but
it does work. So if you had perceptrons that accepted
two variables, each with a weight of minus two, and
the perceptron had a bias of three, it would act
like a nandgate. That's because if both variables are one,
then the final equation you'd get to determine the output
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would be minus two because you multiply the weight of
minus two times the variable of one, and then you
have to add a second minus two because the second
variable is the same way. And then you would add
the bias, which is three. But minus two plus minus
two is minus four. You add in plus three, you
(21:18):
get a minus one is the result minus one is
less than zero, which means they output for the perceptron
must be zero as opposed to one. You get a
false or an off or a zero result. Two positive
inputs create a negative output when a few times you
can say two positives make a negative. Now that means
we can ask progressively more complicated questions, with each perceptron
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handling one aspect of that question and feeding into another
layer of perceptrons. Each perceptron will produce either a positive
or a negative result, so you either get a one
or a zero, and these results will feed into other
neurons in the network, which will use them to perform
their own calculations of their own weights and their own biases.
All of this is to feed those questions through a
(22:05):
network to produce a result, and I should be clear
the weights for each variable along this path can change
from one part of the decision making process to the next.
We're not just talking about identical perceptrons all through the network,
and that last bit is the most important part, because
if this were just a matter of setting biases and
weights and building out a network of perceptrons, there'd be
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nothing special about it, because we already have nannd gates.
They existed before perceptrons. It would just mean that we
have a different way to implement something we could already do,
and finding a new way to do something you were
already doing is rarely super transformative. You might be able
to make it a better way of doing the same thing,
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but in this case it might be less efficient than
the old way. However, there is something else that makes
these perceptrons special, and that's by pairing them with those
special algorithms that Cola and Pets were proposing back in
the forties and fifties. These would be learning algorithms. These
algorithms are instructions that can, based upon external stimuli, dynamically
(23:10):
and automatically tune the weights and biases of perceptrons in
a neural network. In other words, a program can guide
the network so that it learns how to solve problems.
But how well. It all comes down to making small
changes in those weights and biases in order to fine
tune outputs. So let's say we're working on an image
(23:33):
recognition algorithm. That's one of the big things that the
neural engine and Apple's iPhones do. They that's one of
their main purposes. So in our example, let's say we're
training the neural network to recognize handwritten printed lowercase letters.
It's very similar to what McCulla was talking about. But
let's say our model is having trouble differentiating a lowercase
(23:56):
L with a lowercase I. It was just having issues
being able to tell those two apart in particular. Now
we've got a specific example in which our model is
misidentifying an L as an eye. Let's say, in the
hypothetical situation, and so we decide we're gonna make some
minor tweaks in the weights and biases earlier on in
(24:18):
the artificial neural network to guide our network so that
it can more readily tell the difference between l lower
case ls and lower case eyes. And we get our
model closer to being able to tell that difference. We
keep making these small adjustments until we get more consistent output.
The network as a whole is said to quote unquote
(24:38):
learn through this process. It's getting better and creating an
output there's more reflective reality. But there's a bit of
a problem, and anyone who has worked in QA has
probably already spotted what it is. For everybody else. I'm
gonna explain it in just a minute, but first let's
take another quick break to thank our sponsor. So what
(25:05):
was that problem I was talking about before the break? Well,
if you've ever worked in any sort of programming environment,
you know that when you introduce changes in code, you
might fix whatever problem you're focusing on at the moment,
but you might also break something else that's already in
the code. With perceptrons. That happens when you start tweaking
weights and biases, because a small change in one spot
(25:28):
in a network can have sort of a ripple effect
with unintended consequences. So, for example, in our little hypothetical situation,
maybe your new model can better tell the difference between
a lower case L and a lower case I, but
now the lowercase J is giving it problems the way
perceptron's work. Small changes in the network can reduce much
(25:50):
larger variations and output, so it's sort of like the
butterfly effect in action. Computer scientists created a different type
of artificial neuron network that addresses this problem, and this
type is called a sigmoid neuron. Really, I should say
they created a different type of artificial neuron, So the
sigmoid neuron. What the heck is this? Well, from a
(26:12):
high level, sigmoid neurons look kind of like perceptrons, but
while you'd either use either a zero or a one
as the value for an input into a perceptron, a
sigmoid neuron can accept a zero, a one, or any
number in between zero and one. The output a sigmoid
(26:32):
neuron produces is called the logistic function or sigmoid function.
This gets a bit complicated on a surface level, particularly
if like me, you're a little rusty on your algebra
and calculus, but generally speaking, the end result is that
using this type of artificial neuron, you can make small
(26:52):
changes to weights and biases and not create a larger
effect on the ultimate output. You'll still make small adjustments
to the output. There are a lot of resources online
that go into greater detail about sigma neurons. I'm not
going to go into more detail here because without visual
aids and being able to go into algebraic functions, it
gets a little hard for me to explain. But in
(27:15):
your typical neural network, you would have an input layer
and you would have an output layer, So you have
a layer where information comes in, and you would have
the output layer where new information comes out. But between
those two you would have what are called hidden layers.
Then just really means that they're not input or output
there in the middle. Hidden makes it sound like they're
(27:37):
super clandestine and spy worthy and cool, but really they're
just in between input and output. They perform processes on
the inputs they receive, and they passed them on as
outputs to other neurons to have more processes put on
them until you finally get the output. The sort of
networks I've described so far are called feed forward networks,
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and that means pretty much what sounds like. You plug
in and puts the information passes one way through the network,
and you eventually get output as the information continues to move,
and we typically visualize this in a left to right
kind of of display, so you imagine input coming in
from the left side, passing through this network, having various
(28:23):
processes put on it as each of these neurons UH
decides if it counts as a positive or a negative response,
or with sigmoid neurons, some degree in between, and then
plugging that into the next neuron until you finally get
to the output. It always gets fed forward. But that's
not the only type of artificial neural network. There are
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also things called recurrent neural networks, in which neurons fire
at some predetermined amount of time. Then they typically settle
down they're not firing at all, but the next group
of neurons start to fire. This creates kind of a
cascade effect through the network, and occasionally there it could
be neurons that feed back into previous neurons. There's a
(29:06):
feedback loop. It's more challenging to make a powerful learning
algorithm with recurrent neural networks because it gets super duper complicated.
But recurrent neural networks pose potentially huge utility in the future.
So an artificial neural network can be made up as
of as few as a few dozen artificial neurons all
(29:28):
the way up to millions of artificial neurons, and we
trained them through various processes such as back propagation. Now
that's when you take the actual output of the process
and you compare it to what you wanted it to produce,
and then you use the difference between those two results
to make changes to the weights and biases in the network.
(29:48):
So here's an example where training a our network to
recognize pictures of cats, because this has actually been done.
Google famously did this. So you're training your network to
recognize what a cat is based upon a picture, and
you use a picture that you know is a picture
of a cat, so you already know the answer to this.
(30:10):
You're teaching the computer to learn the answer to this.
You know that the answer is cat, and you feed
the image through this system, It analyzes the data, it
gives you an output, and you see how well it did.
Did it correctly identify the image as a cat, did
it assign a certain level of of certainty to its conclusion,
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and if it's far off, you could start making changes
to those weights and biases to help guide the system
into determining, oh, yes, that is a cat. Training a
network multiple times refines this process to the point where
you can start to introduce brand new inputs to the system,
inputs that the system has never encountered before, and get
(30:54):
a reliable result. So with Google's example, you might feed
it thousands or tens of thousands, or hundreds of thousands
or more images of cats, and each time the system
is told now that there is a cat in this image,
and it begins to refine its approach, figuring out which
(31:14):
weights and biases it needs to tweak in order to
get to that result. And then you feed it a
whole group of new images and you don't tell it
if there are cats in those images or not. Then
you leave it to the system to determine are there
cats in these pictures? And if you have trained it properly,
if those weights and biases are actually well tweaked, then
(31:38):
the system should be able to reliably pick out the
pictures that have cats in them. That's the idea. Now,
there's tons more to be said about artificial neural networks,
but i'll give you I've given a quick overview. Let's
let's jump back over to Apple for a second, because
that was the whole purpose of this episode. So what
is a neural engine actually used for. Well, for the iPhone,
(32:00):
it's used mainly in processing speech and image data. It's
the neural engine that can analyze your face, for example,
and then translate your expressions into animated form. You can
create animated emoji this way, So you could use the
little application and create a customized surprise emoji that copies
(32:21):
the way you look when you make a sort of
an exaggerated surprise face. You could do that. The neural
engine takes the incoming data the images it's pulling from
the camera, analyze it, and then helps create an animated
image that mirrors what you did. The neural engine also
analyzes visual data for the purposes of augmented reality. That's
when you overlay digital information on top of a view
(32:44):
of the physical world around you. So with smartphones like
the iPhone, it means holding your phone up and looking
at the world through your phone screen. So the camera
on your phone is giving you a live video feed
of whatever you're pointing the phone at it, and then
you use an augmented reality app, and on top of
(33:04):
that video image your phone will overlay some sort of
digital information. Could be a game, it could be information
about your surroundings. The digital information can appear to be
anchored to the physical space itself. So you could have
an augmented reality application that let's you view a virtual
piece of furniture in your house. And so when you
hold up the phone, you use the app to place
(33:27):
a virtual chair, let's say, in a specific location in
a room, and you can walk around this virtual chair
holding your phone up, and it looks like the chair
is actually there, even as your perspective changes. You can
circle around it and view the chair from all the
different angles as if it were actually sitting there in
the room. It's anchored to its place that you've put
(33:48):
it within the view of the room. The neural engine
is analyzing all this information that's coming in from the
camera and helping the app create the image of the chair,
keeping it the appropriate size and orientation with respect your
viewing angle. And the neural engine can use this ability
to help you go through stuff like your photos. Let's
say you've got an adorable pet, like my doggie Timbolt.
(34:10):
He is adorable. The iPhone can use its neural engine
and image recognition algorithms to return the pictures of your
pet in response to a search query. So my wife,
who has an iPhone, could do this with our dogs.
She could search for the word dog in her photo
app and then she would get countless images of Tibolt.
(34:31):
And I know it works because she's done it. Apple
has included access to the neural engine so that app
developers can actually take advantage of that technology as well.
They'll doubtlessly create new ways to leverage this tech, so
we'll have to keep our eyes open to see what
comes out of it. Neural networks in general are becoming
increasingly important in machine learning and artificial intelligence, so it's
(34:54):
likely to grow as a branch of computer science for
the next several years. And that wraps up this episode.
If you have suggestions for future episodes of tech Stuff,
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(35:15):
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(35:37):
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how staff works dot com ye
Get in touch with technology with tech Stuff from how
stuff works dot com. Hey there, and welcome to tech Stuff.
My name is Jonathan Strickland. I happen to be the
host of this show. I'm also an executive producer at
how stuff Works. And hey, I love all things tech,
and today we're doing a little listener mail request. Dan
(00:27):
wrote in and asked if I might do an episode
about Apple's so called neural engine in its more recent iPhones.
So today we are going to learn what a neural
engine is and what it does. And if you guys,
by the way, have any requests for topics you've always thought, Hey,
I want to have an episode about this particular tech topic. Remember,
(00:49):
you can send those to me by sending an email
to tex Stuff at how stuff works dot com. And
now let's talk about this neural engine. Well, the general
public for heard about this topic back in September two
thousand seventeen, when Apple CEO Tim Cook presented at what
has become an annual tradition for Apple at around that
(01:12):
time of year, pretty much every September is when Apple
will come out and unveil the latest in its line
of iPhone smartphones, and in that would have been the
Iconic iPhone X, the tenth anniversary edition of the iPhone
also the one that's been discontinued now. Cook listed off
(01:32):
a lot of features when he went to that presentation,
but the one we're really interested in today is part
of the phones A eleven micro processor, also called the
A eleven Bionic CPU. The most recent iPhones as of
the recording of this podcast now have the next generation
of that chip, the A twelve, But in both cases,
(01:55):
the neural engine is one of the elements that gets
a lot of coverage. So let's go to the A
eleven since that was the first one to have it.
It's more than just a CPU. It's technically a system
on a chip or s O a C. It's an
ARM sixty four bit chip. But that doesn't really tell
you anything if you're not, you know, deep into the
(02:18):
world of micro processors. So what does that actually mean. Well,
the ARM based part means that it's it's based on
the ARM micro architecture in chip design. So for our
purposes we can simplify this to say, the chips components,
the stuff that's actually on the microprocessor are laid out
(02:38):
in a way that was developed by ARM Holdings, that's
the company behind ARM processors. Now that is different from
the layout you would find in a chip that was
made by Intel, for example. So the architecture part literally
refers to the layout of components in the microprocessor and
how they interact with each other. And generally speaking, companies
(03:02):
that make microprocessors develop an architecture. They do so in
a way that is supposed to maximize the efficiency of
the chips. So if you get the most power for
the least amount of energy input you can with the
least amount of waste, really is the best way of
putting it. You don't want to waste too much and
produce too much heat. And then you typically would then
(03:25):
reduce the size of the various components. And then after
you reduce the size of the components, you might figure
out a new architecture that makes better use of these
smaller components. And this process goes on and on. Intel
calls this the TIC talk methodology. So that's what the
ARM based part means. It's from this particular company following
(03:47):
this particular layout. As for that sixty four bit part,
what does that mean, Well, that refers to the data
width of the arithmetic logic unit or a LU. The
says the part of the processor that actually carries out
those operations on data from computer instructions. So data with
(04:08):
essentially tells you how much information the a L you
can accept or handle at a given time, and it
tells you this in bits. Now, a bit, just to
remind you, is a single unit of computational information, and
it is binary, meaning has two states, which we designate
(04:29):
as being either a zero or a one. Some people
say often on or false and true, but it's zero
and one. The number of bits tells you how big
these actual numbers can get. Before the a L you
can't handle them anymore. So let's say you have an
(04:49):
eight bit chip, because that's a lot easier to talk about.
You would be able to add, subtract, multiply, divide, you know,
the basic arithmetic lot logical operation to eight bit numbers.
With an eight bit chip, Now, a single bit is
a zero or a one, and eight bit number you
can represent as a string of eight eight numbers, either
(05:14):
zeros or ones, So you could have eight zeros in
a row, up to eight ones in a row and
everything in between. So it could be seven zeros and
then a one or it could be six zeros and
then a one and then another zero. You get the point.
With that many combinations, that means you would be able
to go from the typical numbers of zero to two
(05:36):
hundred fifty five. That's with eight bit. However, we're not
talking eight bit. We're talking about a sixty four bit chips.
So now you have sixty four digits in a row
that can be either a zero or one. That provides
you a lot more combinations, which means you could range
(05:57):
in number from zero row to nine quintillion, two hundred
twenty three quadrillion, three hundred seventy two trillion, thirty six billion,
eight hundred fifty four million, seven hundred seventy five thousand,
eight hundred seven. That's a pretty big range. It can
(06:19):
handle way way larger numbers than an eight bit chip.
So that tells you the type of architecture this chip
has and the amount of data it can handle at
a time. The A eleven has six cores, so processors
with multiple cores can work on parts of a problem simultaneously.
(06:40):
If you have something that's called a parallel problem, you
can divide that problem up into different segments and have
different cores tackle it. Two of those six cores are
what Apple calls high performance cores. They have a clock
speed of two point three thirty nine giga hurts uh
in the A eleven, So the clock speed tells you
how many clock cycles a CPU can perform per second.
(07:03):
Two point three nine gigga hurts means that these cores
can each perform two point thirty nine billion clock cycles
per second. Now, clock cycles do not easily translate over
into actions. It's not necessarily one clock cycle per action.
But generally these numbers tell you how much a core
(07:26):
of the processor is able to handle per second, how
many tasks it can do per second, assuming a certain
number of clock cycles per task. Now, these two cores
are referred to as Monsoon. The other four cores are
what Apple refers to as energy efficient cores. They are
not at that same high clock speed. They are meant
(07:47):
to handle more routine tasks. They are called mistral. So
you have Monsoon and Mistral, two Monsoon cores for mistral cores.
But the A eleven is not just a CPU. Also
has a three core graphics processing unit or GPU incorporated
into this chip. And then there are the two processing
(08:08):
cores dedicated specifically to handling tasks related to machine learning algorithms.
This pair of processors are the neural engine. They are
essentially an artificial neural network. And I've talked a little
bit about artificial neural networks before, but we're really going
to try and get an understanding of what makes them
(08:29):
special today, because that's really why neural engine means anything
in the first place. So this means we get to
do a quick history lesson because this is tech stuff,
and of course we have to go into the history.
So here we go back in the nineteen forties and
the nineteen fifties, there were some smart guys named Warren
(08:51):
McCullough who was a neurophysiologist, and another guy named Walter
Pitts who was a computer scientist and a logician, and
they began developing theories that brought together computational science and neuroscience,
in other words, the way machines process information and the
way brains process information, which is different. McCullough wrote a
(09:13):
couple of papers about this, and he asserted that the
basic unit of logic in the brain is the neuron.
So the nerve cell, the brain cell, is your your
basic unit of logic in a brain, so it would
act kind of like a gate or a transistor in
a circuit. And so you might have a transistor being
(09:35):
the smallest unit, not not metric of logic, but the
smallest unit to allow this to happen in a circuit
neurons in the brain. Pets and McCullough began developing computer
algorithms that attempted to guide machines to process information in
a way that was at least conceptually similar to the
way our brains process information. McCullough had proposed that by
(09:57):
doing this, you could train a machine to wreck niye
handwritten characters like numbers or letters, even if those representations
varied in size or style. And I've talked about this
being a challenge in the past as well, that training
a computer to recognize a specific type of image or
(10:18):
a specific thing in an image is challenging. So I
always use coffee mugs as an example. I don't know why,
but I like that that particular one. So we're gonna
go with it again. If you were to create a
computer program where you feed an image of a coffee
mug to the computer program, and you tell the computer
program this image corresponds with this concept called coffee mug.
(10:43):
And the image shows a blue coffee mug and its
handle is pointed toward the right of the perspective of
the viewer. And then you were to feed a different image,
maybe of that same coffee mug, but now at a
different angle. Well, the machine as looking at this as
if it's a totally new thing. It cannot just uh
(11:05):
extricate that information and say, oh, this is also a
coffee mug, or maybe it's a different coffee mug. It's
a different color or a different size or different shape.
The computer doesn't understand the concept of coffee mug. So
how can you teach it this concept? How can you
train it so it recognizes coffee mugs? That was what
(11:25):
McCulloch was looking at. Then you have another guy who
came along, Frank Rosenblat, very smart man, who built on
this work. He developed an artificial neuron called the perceptron. Now,
a perceptron's job is, from a very high level, pretty simple.
It accepts multiple binary inputs. So it accepts inputs that
(11:46):
are either zeros or ones, and then it produces a
single binary output either a zero or a one based
upon processing that information. So let's say you want to
create a program that can help you decide which restaurant
you want to go to, and you've come up with
three criteria that you think are really important in order
(12:07):
for you to make this decision. And the three criteria
you have are is the restaurant within a twenty minute
drive or less? So, is it relatively close? Will a
meal cost less than fifty dollars for two people to
have dinner there? And does the restaurant serve tacos? Those
are your three points of criteria, and you can represent
(12:30):
each of those variables with a binary figure. So, for example,
you could say that if the restaurant is closer than
a twenty minute drive, if it is nearby, you represent
that variable with a one. If it is further away
than that, it's a zero. If the dinner for two
is cheaper than fifty dollars, that's a one. If it's
(12:50):
more expensive, it's a zero. And if it serves tacos,
it's a one. And if it does not serve tacos,
it's a big fat zero. Then you have a list
of various restaurants you could feed each restaurant through your
criteria and see how they do. Uh, And then you
could narrow your choices this way, and perhaps there is
no single restaurant that meets all those criteria, so you
(13:13):
really should take another step. And that's where Rosenblatt introduces
the concept of weights, where you you change how important
each of the criteria are in relation to each other.
Weights are real numbers that indicate the importance of particular criterion.
So you want, let's say all those three criteria you've identified,
(13:36):
the distance, the cost, and whether or not they have tacos.
You have decided the most critical piece of information is
whether or not the restaurant serves tacos. So you could
then assign a greater weight to that criterion, saying this
is more important to me, and that will influence the
output of the neuron. You must also determine a threshold
(13:58):
value for the decision. In other words, you say, in
order to produce a positive result to tell me, yes,
this is a restaurant you should go to, you must
at least meet this threshold. That's the minimum value the
calculation has to meet or exceed in order to produce
a go to this restaurant result. I'll explain a bit
(14:19):
more about this in just a second, But first I'm
going to take a quick break and thank our sponsors.
That threshold value that I mentioned before the break is
really important because it tells your model what sort of
results count as valid versus not valid. So let's say
(14:43):
I've waited the criteria so that the distance to the
restaurant and the expense of the meal each have a
weight of two, but the presence of tacos is a six.
That's how important I think tacos are. And I've said
a threshold of four. Well, that means that if the
restaurant is relatively close and it's relatively inexpensive, it's going
(15:04):
to pass my criteria because I've given a weight of
two for both of those and added together that's four.
It equals the threshold. Good to go. But even if
the restaurant is far away and even if it's expensive,
if it serves tacos, it still passes my criteria because
I gave the tacos a weight of six. Raising the
threshold value reduces the number of valid restaurants. So if
(15:29):
I make the threshold eight instead of four, now the
only way I can get a valid result a result
of yes, go to this restaurant is if the restaurant
has tacos and it's either close by, or it's inexpensive,
or both. And if I said the threshold were ten,
all three criteria would need to be met for this
(15:51):
option to be valid. Now, an artificial intelligence for the
purposes of notation, many people will move the threshold value
to the other side of the equation, and in this
case we now call it a bias, and a bias
essentially is a measurement to tell you how easy or
difficult it is to get the perceptron to fire off
a positive value. If you have a big positive bias,
(16:14):
that means it's easier for the perceptron to produce a
positive result a one. A large negative bias does the opposite,
and thus you would get a zero. So we can
write out the perceptron's rules like this. Take the value
of a variable which is either going to be a
zero or a one. It will be binary. You multiply
(16:36):
the value of this variable by the weight of that variable,
and weights can be different values. Let's say that the
distance and expense are both weighted at two. Tacos gets
a big hefty six. You're going to add your various
(16:56):
weighted variable results together, and then you add the I
s for the perceptron. And in our example, the bias
is a minus six. That's to tell us that in
order for this perceptron to fire, you have to you
have to be able to factor in that minus six
and beat it. So if after adding these elements together,
(17:18):
you get a result that is zero or lower, the
output is a zero or a negative, saying, don't go
to this restaurant. So after adding that negative six, if
you have a zero or less, you don't go. If
you get a result that's greater than zero, it's a
positive result, it says, go to that restaurant. So here
in our hypothetical perceptron, we've decided on a bias of
minus six, and we take our three variables as we
(17:40):
examine a single restaurant. So this restaurant is twenty five
minutes away. So that means for our first variable, which
is all about distance, it gets a zero because it
is further than twenty minutes away. So that variable is
a zero. And we multiply the variable times the weight.
The weight is too for that particular variable two time
zero is zero. Then I look and I see that
(18:05):
dinner for two of that restaurant's gonna set me back
thirty dollars, but that's below the limit we had set
of fifty dollars. So that means the value of the
variable is one. It is cheaper than fifty dollars, so
that gets a one. The weight for this variable is
to so multiply the weight times of variable two times
one is two. Then we have the question does the
(18:26):
restaurants serve tacos? And I know you're dying to know this.
I'm glad to report the restaurant does in fact serve tacos,
And that means that the variable is a one. It's positive,
and we waited this variable very heavily with a six,
So six times one is six. Now we have to
add all of those results together, so we have zero
(18:49):
from the first one, too, from the second one, six
from the third one. Add that together you get eight.
Now we have to add in the bias, and the
bias for this perceptron is a minus six. Eight plus
minus six gives us a final value of two. Two
is greater than zero. So by the rules we have established,
(19:09):
the perceptron says this is a positive result and fires
off a one. So the restaurant we fed to the
perceptron met the criteria based on that bias. Now, if
our bias had been minus ten or minus nine, we
would have not produced this positive result. We have gotten
a zero or negative number and it would have said no.
(19:31):
So that bias is very important, as is the weight
of the various variables. And that is one neuron. Now
you can actually create layers of neurons. That's why we
call it an artificial neural network, not just an artificial neuron.
And by doing that you can have results from one
neuron's decisions feed directly into another neuron. Also, a perceptron
(19:55):
can perform as a type of logical gait called a
name end gate in a n D that stands for
not and it's a type of logical gate that can
produce a false or negative output if all its inputs
are true or positive. So, in other words, with the
(20:16):
right weights and biases, a perceptron will produce an output
of zero if all of its inputs are ones. The
nand gate in computer science is a universal gate because
you can use different creations and combinations of nand gates
and build any kind of computation. You just have to
(20:36):
link them together properly in order to do it. It's
not always the most efficient way to do this, but
it does work. So if you had perceptrons that accepted
two variables, each with a weight of minus two, and
the perceptron had a bias of three, it would act
like a nandgate. That's because if both variables are one,
then the final equation you'd get to determine the output
(20:58):
would be minus two because you multiply the weight of
minus two times the variable of one, and then you
have to add a second minus two because the second
variable is the same way. And then you would add
the bias, which is three. But minus two plus minus
two is minus four. You add in plus three, you
(21:18):
get a minus one is the result minus one is
less than zero, which means they output for the perceptron
must be zero as opposed to one. You get a
false or an off or a zero result. Two positive
inputs create a negative output when a few times you
can say two positives make a negative. Now that means
we can ask progressively more complicated questions, with each perceptron
(21:42):
handling one aspect of that question and feeding into another
layer of perceptrons. Each perceptron will produce either a positive
or a negative result, so you either get a one
or a zero, and these results will feed into other
neurons in the network, which will use them to perform
their own calculations of their own weights and their own biases.
All of this is to feed those questions through a
(22:05):
network to produce a result, and I should be clear
the weights for each variable along this path can change
from one part of the decision making process to the next.
We're not just talking about identical perceptrons all through the network,
and that last bit is the most important part, because
if this were just a matter of setting biases and
weights and building out a network of perceptrons, there'd be
(22:27):
nothing special about it, because we already have nannd gates.
They existed before perceptrons. It would just mean that we
have a different way to implement something we could already do,
and finding a new way to do something you were
already doing is rarely super transformative. You might be able
to make it a better way of doing the same thing,
(22:48):
but in this case it might be less efficient than
the old way. However, there is something else that makes
these perceptrons special, and that's by pairing them with those
special algorithms that Cola and Pets were proposing back in
the forties and fifties. These would be learning algorithms. These
algorithms are instructions that can, based upon external stimuli, dynamically
(23:10):
and automatically tune the weights and biases of perceptrons in
a neural network. In other words, a program can guide
the network so that it learns how to solve problems.
But how well. It all comes down to making small
changes in those weights and biases in order to fine
tune outputs. So let's say we're working on an image
(23:33):
recognition algorithm. That's one of the big things that the
neural engine and Apple's iPhones do. They that's one of
their main purposes. So in our example, let's say we're
training the neural network to recognize handwritten printed lowercase letters.
It's very similar to what McCulla was talking about. But
let's say our model is having trouble differentiating a lowercase
(23:56):
L with a lowercase I. It was just having issues
being able to tell those two apart in particular. Now
we've got a specific example in which our model is
misidentifying an L as an eye. Let's say, in the
hypothetical situation, and so we decide we're gonna make some
minor tweaks in the weights and biases earlier on in
(24:18):
the artificial neural network to guide our network so that
it can more readily tell the difference between l lower
case ls and lower case eyes. And we get our
model closer to being able to tell that difference. We
keep making these small adjustments until we get more consistent output.
The network as a whole is said to quote unquote
(24:38):
learn through this process. It's getting better and creating an
output there's more reflective reality. But there's a bit of
a problem, and anyone who has worked in QA has
probably already spotted what it is. For everybody else. I'm
gonna explain it in just a minute, but first let's
take another quick break to thank our sponsor. So what
(25:05):
was that problem I was talking about before the break? Well,
if you've ever worked in any sort of programming environment,
you know that when you introduce changes in code, you
might fix whatever problem you're focusing on at the moment,
but you might also break something else that's already in
the code. With perceptrons. That happens when you start tweaking
weights and biases, because a small change in one spot
(25:28):
in a network can have sort of a ripple effect
with unintended consequences. So, for example, in our little hypothetical situation,
maybe your new model can better tell the difference between
a lower case L and a lower case I, but
now the lowercase J is giving it problems the way
perceptron's work. Small changes in the network can reduce much
(25:50):
larger variations and output, so it's sort of like the
butterfly effect in action. Computer scientists created a different type
of artificial neuron network that addresses this problem, and this
type is called a sigmoid neuron. Really, I should say
they created a different type of artificial neuron, So the
sigmoid neuron. What the heck is this? Well, from a
(26:12):
high level, sigmoid neurons look kind of like perceptrons, but
while you'd either use either a zero or a one
as the value for an input into a perceptron, a
sigmoid neuron can accept a zero, a one, or any
number in between zero and one. The output a sigmoid
(26:32):
neuron produces is called the logistic function or sigmoid function.
This gets a bit complicated on a surface level, particularly
if like me, you're a little rusty on your algebra
and calculus, but generally speaking, the end result is that
using this type of artificial neuron, you can make small
(26:52):
changes to weights and biases and not create a larger
effect on the ultimate output. You'll still make small adjustments
to the output. There are a lot of resources online
that go into greater detail about sigma neurons. I'm not
going to go into more detail here because without visual
aids and being able to go into algebraic functions, it
gets a little hard for me to explain. But in
(27:15):
your typical neural network, you would have an input layer
and you would have an output layer, So you have
a layer where information comes in, and you would have
the output layer where new information comes out. But between
those two you would have what are called hidden layers.
Then just really means that they're not input or output
there in the middle. Hidden makes it sound like they're
(27:37):
super clandestine and spy worthy and cool, but really they're
just in between input and output. They perform processes on
the inputs they receive, and they passed them on as
outputs to other neurons to have more processes put on
them until you finally get the output. The sort of
networks I've described so far are called feed forward networks,
(28:01):
and that means pretty much what sounds like. You plug
in and puts the information passes one way through the network,
and you eventually get output as the information continues to move,
and we typically visualize this in a left to right
kind of of display, so you imagine input coming in
from the left side, passing through this network, having various
(28:23):
processes put on it as each of these neurons UH
decides if it counts as a positive or a negative response,
or with sigmoid neurons, some degree in between, and then
plugging that into the next neuron until you finally get
to the output. It always gets fed forward. But that's
not the only type of artificial neural network. There are
(28:44):
also things called recurrent neural networks, in which neurons fire
at some predetermined amount of time. Then they typically settle
down they're not firing at all, but the next group
of neurons start to fire. This creates kind of a
cascade effect through the network, and occasionally there it could
be neurons that feed back into previous neurons. There's a
(29:06):
feedback loop. It's more challenging to make a powerful learning
algorithm with recurrent neural networks because it gets super duper complicated.
But recurrent neural networks pose potentially huge utility in the future.
So an artificial neural network can be made up as
of as few as a few dozen artificial neurons all
(29:28):
the way up to millions of artificial neurons, and we
trained them through various processes such as back propagation. Now
that's when you take the actual output of the process
and you compare it to what you wanted it to produce,
and then you use the difference between those two results
to make changes to the weights and biases in the network.
(29:48):
So here's an example where training a our network to
recognize pictures of cats, because this has actually been done.
Google famously did this. So you're training your network to
recognize what a cat is based upon a picture, and
you use a picture that you know is a picture
of a cat, so you already know the answer to this.
(30:10):
You're teaching the computer to learn the answer to this.
You know that the answer is cat, and you feed
the image through this system, It analyzes the data, it
gives you an output, and you see how well it did.
Did it correctly identify the image as a cat, did
it assign a certain level of of certainty to its conclusion,
(30:34):
and if it's far off, you could start making changes
to those weights and biases to help guide the system
into determining, oh, yes, that is a cat. Training a
network multiple times refines this process to the point where
you can start to introduce brand new inputs to the system,
inputs that the system has never encountered before, and get
(30:54):
a reliable result. So with Google's example, you might feed
it thousands or tens of thousands, or hundreds of thousands
or more images of cats, and each time the system
is told now that there is a cat in this image,
and it begins to refine its approach, figuring out which
(31:14):
weights and biases it needs to tweak in order to
get to that result. And then you feed it a
whole group of new images and you don't tell it
if there are cats in those images or not. Then
you leave it to the system to determine are there
cats in these pictures? And if you have trained it properly,
if those weights and biases are actually well tweaked, then
(31:38):
the system should be able to reliably pick out the
pictures that have cats in them. That's the idea. Now,
there's tons more to be said about artificial neural networks,
but i'll give you I've given a quick overview. Let's
let's jump back over to Apple for a second, because
that was the whole purpose of this episode. So what
is a neural engine actually used for. Well, for the iPhone,
(32:00):
it's used mainly in processing speech and image data. It's
the neural engine that can analyze your face, for example,
and then translate your expressions into animated form. You can
create animated emoji this way, So you could use the
little application and create a customized surprise emoji that copies
(32:21):
the way you look when you make a sort of
an exaggerated surprise face. You could do that. The neural
engine takes the incoming data the images it's pulling from
the camera, analyze it, and then helps create an animated
image that mirrors what you did. The neural engine also
analyzes visual data for the purposes of augmented reality. That's
when you overlay digital information on top of a view
(32:44):
of the physical world around you. So with smartphones like
the iPhone, it means holding your phone up and looking
at the world through your phone screen. So the camera
on your phone is giving you a live video feed
of whatever you're pointing the phone at it, and then
you use an augmented reality app, and on top of
(33:04):
that video image your phone will overlay some sort of
digital information. Could be a game, it could be information
about your surroundings. The digital information can appear to be
anchored to the physical space itself. So you could have
an augmented reality application that let's you view a virtual
piece of furniture in your house. And so when you
hold up the phone, you use the app to place
(33:27):
a virtual chair, let's say, in a specific location in
a room, and you can walk around this virtual chair
holding your phone up, and it looks like the chair
is actually there, even as your perspective changes. You can
circle around it and view the chair from all the
different angles as if it were actually sitting there in
the room. It's anchored to its place that you've put
(33:48):
it within the view of the room. The neural engine
is analyzing all this information that's coming in from the
camera and helping the app create the image of the chair,
keeping it the appropriate size and orientation with respect your
viewing angle. And the neural engine can use this ability
to help you go through stuff like your photos. Let's
say you've got an adorable pet, like my doggie Timbolt.
(34:10):
He is adorable. The iPhone can use its neural engine
and image recognition algorithms to return the pictures of your
pet in response to a search query. So my wife,
who has an iPhone, could do this with our dogs.
She could search for the word dog in her photo
app and then she would get countless images of Tibolt.
(34:31):
And I know it works because she's done it. Apple
has included access to the neural engine so that app
developers can actually take advantage of that technology as well.
They'll doubtlessly create new ways to leverage this tech, so
we'll have to keep our eyes open to see what
comes out of it. Neural networks in general are becoming
increasingly important in machine learning and artificial intelligence, so it's
(34:54):
likely to grow as a branch of computer science for
the next several years. And that wraps up this episode.
If you have suggestions for future episodes of tech Stuff,
maybe it's a technology, a person in tech company, anything
like that, Send me an email the addresses tech Stuff
at how stuff works dot com. You can draw me
a line on Facebook or Twitter. The handle for both
(35:15):
of those is tech Stuff H s W. Don't forget,
we have a merchandise store over at t public dot
com slash tech stuff. That's T E E public dot
com slash tech stuff. You can go and get your uh,
your caption test, the prove you're not a robot sticker
or T shirt or tote bag or whatever type of
(35:37):
thing you would like that on. It's pretty cool, So
go check that out, and don't forget to follow us
on Instagram and I'll talk to you again really soon.
For more on this and thousands of other topics, visit
how staff works dot com ye
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Oz Woloshyn
Karah Preiss
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